Master Machine Learning with Python: Essential Tools and Techniques with LSET Industry Expert
Machine Learning has become a vast field in computer science. It is basically getting things done by the computers without explicitly programming them. It has given us so many technologies self-driving car, speech recognition, web recommendation engines, etc.
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*Modules of our curriculum are subject to change. We update our curriculum based on the new releases of the libraries, frameworks, Software, etc. Students will be informed about the final curriculum in the course induction class.
Machine Learning, refers to a process of analysing data for training or building models. It is everywhere; from Amazon product recommendations to self-driven cars, it has great value throughout. As per the latest survey, the machine learning market is expected to grow by 43% by 2024. This revolution has greatly enhanced the demand for machine learning professionals.
Machine learning jobs have had a significant growth rate of 75% in the last four years, and the industry is rapidly continuously. In this course, we will work on your core skills like Machine Learning, and Learn specific topics like NLP, Reinforcement Learning, Deep Learning, and a lot more.
Machine Learning is a process of analysing data for training and building models. ML is just everywhere; from self-driven cars to Amazon product recommendations, it holds great value throughout. As per the latest survey, the global machine learning market is expected to grow by 43% by the year 2024. This revolution has increased the demand for machine learning professionals to a huge extent.
Machine learning and Artificial Intelligence jobs have had a significant growth rate of 75% in the last four years, and the industry is growing rapidly. The average salary of an ML professional is £52,000. A career in the Machine learning domain offers excellent growth, job satisfaction, insanely high salary, but it is a complex and challenging process.
Python programming language: It is an interpreted high-level programming language used for web development, machine learning, AI, ML, and a lot more. It provides a clear approach to programmers to write a clear and logical approach.
Pandas is a software library used for Data Analysis and manipulation. It offers operations and data structure and operations for manipulating time series and numerical tables.
NumPy: it is a python library that consists of the multidimensional array and a collection of mathematical functions to operate on this array
Matplotlib is a python library that makes matplotlib work like MATLAB. It provides an object-oriented API for inculcating plots into the application using GUI.
Plotly is an open-source plotting library that supports a wide range of scientific, financial, and geographical use cases.
SciKit-Learn is a Machine Learning library for the Python programming language. It features various regression, classification, and clustering algorithms.
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Join the Machine Learning Advanced Certificate course to start creating ML algorithms in Python and R with data science educators. Become a seasoned machine learning expert with LSET’s practical and project-based learning environment.
*Modules of our curriculum are subject to change. We update our curriculum based on the new releases of the libraries, frameworks, Software, etc. Students will be informed about the final curriculum in the course induction class.
We love to answer questions, empower students, and motivate professionals. Feel free to fill out the form and clear up your doubts related to our Machine learning Course
If you are willing to build your career in Machine Learning. Then this course is exclusively for you!
This course will help you learn about complex theory, algorithms, and coding libraries in a much simpler way. We will be your guide and help you ace into the World of Machine Learning.
With every tutorial, you will learn new skills and improve your understanding of this lucrative sub-field of Data Science. This course is exciting and challenging, but at the same time, we dive deep into the concepts of Machine Learning.
Following are the steps involved in the LSET’s project-based learning;
Step 1: Project Idea Discussion
In this step, students get introduced to the problem and develop a strategy to build the solution.
Step 2: Build Product Backlog
This step requires students to enhance the existing starter product backlog available in the project. This helps students to think about real-life business requirements and formulate them in good user stories.
Step 3: Design Releases and Sprints
In this step, students define software releases and plan sprints for each release. Students must go through sprint planning individually and learn about story points and velocity.
Step 4: Unit and Integration Tests
In this step, students learn to write unit tests to ensure every application part works fine.
Step 5: Use CICD to Deploy
In this step, students learn to use CICD (Continuous Integration Continuous Delivery) pipeline to build their application as a docker image and deploy it to Kubernetes.
London has been a leading international financial centre since the 19th century. In recent years, London has seen many FinTech start-ups and significant innovations in the banking sector. This project aims to introduce students to the financial industry and technologies used to handle billions of daily transactions. As part of this project, students will learn the current technological advances and build up their knowledge to start a simple banking application. This application uses agile project management practices to build basic functionality. Students will be presented with user stories to create the initial project backlog. Students need to enhance this backlog by adding more relevant user stories and working on them.
LSET emphasises project-based learning as it allows the students to master the course content by going through near real-world work experience. LSET projects are carefully designed to teach the industry-required skills and mindset. It motivates the students on various essential aspects like learning to work in teams, improving communication with peers, taking the initiative to look for innovative solutions, enhancing problem-solving skills, understanding the end user requirements to build user-specific products, etc.
Capstone Projects build students’ confidence in handling projects and applying their newly learned skills to solve real-world problems. This allows the students to reflect upon their learning and find the opportunity to get the most out of the course. Learn more about Capstone Projects here.
Start Your Journey to becoming a Professional Machine Learning Expert
LSET could provide the perfect headstart to start your career in Machine Learning with Python.
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